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Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.

Huang Huang1, Wei Lyu2, Md Mahmud Hasan3

  • 1Department of Health Management, Economics and Policy, School of Public Health, Augusta University, 2500 Walton Way, Science Hall, E-1031, Augusta, GA, 30904, United States, 1 8595519185.

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Summary

Machine learning (ML) adoption in US hospitals is high, with 75% using ML in electronic health records (EHRs). Key factors influencing adoption include hospital size, non-profit status, and EHR vendor contracts, raising concerns about digital equity.

Keywords:
artificial intelligenceelectronic health recordhealth information technology adoptionmachine learningorganizational behavior

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Hospital Administration

Background:

  • Machine learning (ML) adoption is increasing in US healthcare, particularly within electronic health record (EHR) systems.
  • The determinants of ML integration and its relationship with hospital characteristics require further empirical investigation.

Purpose of the Study:

  • To assess the current adoption status of ML within EHR systems in US general acute care hospitals.
  • To identify specific hospital characteristics associated with the implementation of ML functions.

Main Methods:

  • Utilized linked data from the American Hospital Association Annual Survey (2022-2023) and its Information Technology Supplement Survey (2023-2024).
  • Included 2562 general and acute care hospitals, with 4055 observations over two years.
  • Employed inverse probability weighting, descriptive statistics, and multivariate logistic regression to analyze ML adoption patterns and associated hospital characteristics.

Main Results:

  • Approximately 75% of hospitals adopted ML functions in EHRs, often integrating both clinical and operational applications.
  • Predicting inpatient risks and outpatient follow-ups were the most common ML functions.
  • Factors positively associated with ML adoption included non-profit status, larger hospital size, metropolitan location, contracting with leading EHR vendors, and health system affiliation.

Conclusions:

  • Hospital ML adoption is driven by organizational resources and strategic priorities, potentially exacerbating digital inequities.
  • Limited ML model evaluation practices necessitate enhanced regulatory oversight and support for underresourced facilities.
  • Addressing disparities in ML adoption and oversight is crucial for equitable, safe, and effective implementation of AI in healthcare.